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ORCIDHongyang Gao
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2020 – today
- 2024
[c22]Benjamin Steenhoek
, Hongyang Gao, Wei Le:
Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection. ICSE 2024: 16:1-16:13- [c21]Shibbir Ahmed, Hongyang Gao, Hridesh Rajan:
Inferring Data Preconditions from Deep Learning Models for Trustworthy Prediction in Deployment. ICSE 2024: 38:1-38:13 - [i23]Shibbir Ahmed, Hongyang Gao, Hridesh Rajan:
Inferring Data Preconditions from Deep Learning Models for Trustworthy Prediction in Deployment. CoRR abs/2401.14628 (2024) - [i22]Zhaoning Yu, Hongyang Gao:
MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generation. CoRR abs/2405.12519 (2024) - [i21]Zhaoning Yu, Xiangyang Xu, Hongyang Gao:
G2T-LLM: Graph-to-Tree Text Encoding for Molecule Generation with Fine-Tuned Large Language Models. CoRR abs/2410.02198 (2024) - 2023
- [j6]Yiming Zhu, Cairong Wang, Chenyu Dong, Ke Zhang, Hongyang Gao, Chun Yuan:
High-Frequency Normalizing Flow for Image Rescaling. IEEE Trans. Image Process. 32: 6223-6233 (2023) - [c20]Tiancheng Zhou, Zachary Glanz, Mei Liu, Jiang Bian, Rui Yin, Hongyang Gao:
HSELDA: Heterogeneous Sub-Graph Learning for lncRNA-Disease Associations Prediction. BIBM 2023: 1798-1805 - [c19]Tianxiang Gao, Xiaokai Huo, Hailiang Liu, Hongyang Gao:
Wide Neural Networks as Gaussian Processes: Lessons from Deep Equilibrium Models. NeurIPS 2023 - [c18]Siyuan Sun, Hongyang Gao:
Meta-AdaM: An Meta-Learned Adaptive Optimizer with Momentum for Few-Shot Learning. NeurIPS 2023 - [i20]Tianxiang Gao, Xiaokai Huo, Hailiang Liu, Hongyang Gao:
Wide Neural Networks as Gaussian Processes: Lessons from Deep Equilibrium Models. CoRR abs/2310.10767 (2023) - [i19]Zhaoning Yu, Hongyang Gao:
MotifPiece: A Data-Driven Approach for Effective Motif Extraction and Molecular Representation Learning. CoRR abs/2312.15387 (2023) - 2022
- [j5]Hongyang Gao, Shuiwang Ji:
Graph U-Nets. IEEE Trans. Pattern Anal. Mach. Intell. 44(9): 4948-4960 (2022) - [c17]Tianxiang Gao, Hailiang Liu, Jia Liu, Hridesh Rajan, Hongyang Gao:
A global convergence theory for deep ReLU implicit networks via over-parameterization. ICLR 2022 - [c16]Zhaoning Yu, Hongyang Gao:
Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks. ICML 2022: 25581-25594 - [i18]Zhaoning Yu, Hongyang Gao:
MotifExplainer: a Motif-based Graph Neural Network Explainer. CoRR abs/2202.00519 (2022) - [i17]Zhaoning Yu, Hongyang Gao:
Molecular Graph Representation Learning via Heterogeneous Motif Graph Construction. CoRR abs/2202.00529 (2022) - [i16]Tianxiang Gao, Hongyang Gao:
Gradient Descent Optimizes Infinite-Depth ReLU Implicit Networks with Linear Widths. CoRR abs/2205.07463 (2022) - [i15]Tianxiang Gao, Hongyang Gao:
On the optimization and generalization of overparameterized implicit neural networks. CoRR abs/2209.15562 (2022) - [i14]Benjamin Steenhoek, Wei Le, Hongyang Gao:
DeepDFA: Dataflow Analysis-Guided Efficient Graph Learning for Vulnerability Detection. CoRR abs/2212.08108 (2022) - 2021
- [j4]Hongyang Gao, Zhengyang Wang, Lei Cai, Shuiwang Ji:
ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions. IEEE Trans. Pattern Anal. Mach. Intell. 43(8): 2570-2581 (2021) - [j3]Hongyang Gao, Yi Liu, Shuiwang Ji:
Topology-Aware Graph Pooling Networks. IEEE Trans. Pattern Anal. Mach. Intell. 43(12): 4512-4518 (2021) - [i13]Hongyang Gao, Yi Liu, Xuan Zhang, Shuiwang Ji:
Sent2Matrix: Folding Character Sequences in Serpentine Manifolds for Two-Dimensional Sentence. CoRR abs/2103.08387 (2021) - [i12]Tianxiang Gao, Hailiang Liu, Jia Liu, Hridesh Rajan, Hongyang Gao:
A global convergence theory for deep ReLU implicit networks via over-parameterization. CoRR abs/2110.05645 (2021) - 2020
- [j2]Hongyang Gao, Hao Yuan, Zhengyang Wang, Shuiwang Ji:
Pixel Transposed Convolutional Networks. IEEE Trans. Pattern Anal. Mach. Intell. 42(5): 1218-1227 (2020) - [c15]Hongyang Gao, Lei Cai, Shuiwang Ji:
Adaptive Convolutional ReLUs. AAAI 2020: 3914-3921 - [c14]Yang Liu, Daolian Jiang, Kun Li, Changsong Wang, Yongjian Liu, Lei Wang, Peng Wang, Hongyang Gao:
A low-power and low-cost battery equalizing circuit topology. EITCE 2020: 1034-1038 - [c13]Kun Li, Shijie Zhang, Qing Liu, Junyou Liu, Peng Wang, Hongyang Gao, Heming Wang, Hongzheng Wang:
Design of Wireless Power Transmission System with Double D-coil. EITCE 2020: 1049-1054 - [c12]Kun Li, Junyou Liu, Shijie Zhang, Yang Liu, Peng Wang, Hongyang Gao:
Circuit parameter optimization of class E power amplifier for wireless power transmission applications. EITCE 2020: 1162-1166 - [c11]Hongyang Gao, Zhengyang Wang, Shuiwang Ji:
Kronecker Attention Networks. KDD 2020: 229-237 - [c10]Meng Liu, Hongyang Gao, Shuiwang Ji:
Towards Deeper Graph Neural Networks. KDD 2020: 338-348 - [i11]Hongyang Gao, Zhengyang Wang, Shuiwang Ji:
Kronecker Attention Networks. CoRR abs/2007.08442 (2020) - [i10]Meng Liu, Hongyang Gao, Shuiwang Ji:
Towards Deeper Graph Neural Networks. CoRR abs/2007.09296 (2020) - [i9]Hongyang Gao, Yi Liu, Shuiwang Ji:
Topology-Aware Graph Pooling Networks. CoRR abs/2010.09834 (2020)
2010 – 2019
- 2019
- [j1]Yongsheng Zhu, Hongyang Gao, Junming Xiao, Boyang Qu, Fanbing Zhu, Lu Yang:
Dynamic Multi-Objective Dispatch Considering Wind Power and Electric Vehicles With Probabilistic Characteristics. IEEE Access 7: 185634-185653 (2019) - [c9]Hongyang Gao, Shuiwang Ji:
Graph U-Nets. ICML 2019: 2083-2092 - [c8]Hongyang Gao, Shuiwang Ji:
Graph Representation Learning via Hard and Channel-Wise Attention Networks. KDD 2019: 741-749 - [c7]Lei Cai, Hongyang Gao, Shuiwang Ji:
Multi-Stage Variational Auto-Encoders for Coarse-to-Fine Image Generation. SDM 2019: 630-638 - [c6]Hongyang Gao, Yongjun Chen, Shuiwang Ji:
Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations. WWW 2019: 2743-2749 - [i8]Hongyang Gao, Yongjun Chen, Shuiwang Ji:
Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations. CoRR abs/1901.06965 (2019) - [i7]Hongyang Gao, Shuiwang Ji:
Graph U-Nets. CoRR abs/1905.05178 (2019) - [i6]Hongyang Gao, Shuiwang Ji:
Graph Representation Learning via Hard and Channel-Wise Attention Networks. CoRR abs/1907.04652 (2019) - 2018
- [c5]Lei Cai, Zhengyang Wang, Hongyang Gao, Dinggang Shen, Shuiwang Ji:
Deep Adversarial Learning for Multi-Modality Missing Data Completion. KDD 2018: 1158-1166 - [c4]Yongjun Chen, Hongyang Gao, Lei Cai, Min Shi, Dinggang Shen, Shuiwang Ji:
Voxel Deconvolutional Networks for 3D Brain Image Labeling. KDD 2018: 1226-1234 - [c3]Hongyang Gao, Zhengyang Wang, Shuiwang Ji:
Large-Scale Learnable Graph Convolutional Networks. KDD 2018: 1416-1424 - [c2]Hongyang Gao, Zhengyang Wang, Shuiwang Ji:
ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions. NeurIPS 2018: 5203-5211 - [i5]Hongyang Gao, Zhengyang Wang, Shuiwang Ji:
Large-Scale Learnable Graph Convolutional Networks. CoRR abs/1808.03965 (2018) - [i4]Hongyang Gao, Zhengyang Wang, Shuiwang Ji:
ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions. CoRR abs/1809.01330 (2018) - 2017
- [c1]Hongyang Gao, Shuiwang Ji:
Efficient and Invariant Convolutional Neural Networks for Dense Prediction. ICDM 2017: 871-876 - [i3]Hongyang Gao, Hao Yuan, Zhengyang Wang, Shuiwang Ji:
Pixel Deconvolutional Networks. CoRR abs/1705.06820 (2017) - [i2]Lei Cai, Hongyang Gao, Shuiwang Ji:
Multi-Stage Variational Auto-Encoders for Coarse-to-Fine Image Generation. CoRR abs/1705.07202 (2017) - [i1]Hongyang Gao, Shuiwang Ji:
Efficient and Invariant Convolutional Neural Networks for Dense Prediction. CoRR abs/1711.09064 (2017)
Coauthor Index
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